Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations88889
Missing cells565032
Missing cells (%)20.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 MiB
Average record size in memory248.0 B

Variable types

Text12
Categorical9
DateTime2
Unsupported2
Boolean1
Numeric5

Alerts

Aircraft.damage is highly overall correlated with Investigation.TypeHigh correlation
Engine.Type is highly overall correlated with Number.of.Engines and 1 other fieldsHigh correlation
FAR.Description is highly overall correlated with Investigation.Type and 1 other fieldsHigh correlation
Investigation.Type is highly overall correlated with Aircraft.damage and 2 other fieldsHigh correlation
Number.of.Engines is highly overall correlated with Engine.Type and 1 other fieldsHigh correlation
Purpose.of.flight is highly overall correlated with ScheduleHigh correlation
Schedule is highly overall correlated with Engine.Type and 4 other fieldsHigh correlation
Total.Fatal.Injuries is highly overall correlated with Total.UninjuredHigh correlation
Total.Uninjured is highly overall correlated with Total.Fatal.InjuriesHigh correlation
Investigation.Type is highly imbalanced (74.2%)Imbalance
Aircraft.damage is highly imbalanced (51.7%)Imbalance
Aircraft.Category is highly imbalanced (79.3%)Imbalance
Amateur.Built is highly imbalanced (54.6%)Imbalance
Engine.Type is highly imbalanced (73.8%)Imbalance
FAR.Description is highly imbalanced (55.2%)Imbalance
Purpose.of.flight is highly imbalanced (54.1%)Imbalance
Weather.Condition is highly imbalanced (76.0%)Imbalance
Latitude has 54507 (61.3%) missing valuesMissing
Longitude has 54516 (61.3%) missing valuesMissing
Airport.Code has 38757 (43.6%) missing valuesMissing
Airport.Name has 36185 (40.7%) missing valuesMissing
Injury.Severity has 1000 (1.1%) missing valuesMissing
Aircraft.damage has 3194 (3.6%) missing valuesMissing
Aircraft.Category has 56602 (63.7%) missing valuesMissing
Registration.Number has 1382 (1.6%) missing valuesMissing
Number.of.Engines has 6084 (6.8%) missing valuesMissing
Engine.Type has 7096 (8.0%) missing valuesMissing
FAR.Description has 56866 (64.0%) missing valuesMissing
Schedule has 76307 (85.8%) missing valuesMissing
Purpose.of.flight has 6192 (7.0%) missing valuesMissing
Air.carrier has 72241 (81.3%) missing valuesMissing
Total.Fatal.Injuries has 11401 (12.8%) missing valuesMissing
Total.Serious.Injuries has 12510 (14.1%) missing valuesMissing
Total.Minor.Injuries has 11933 (13.4%) missing valuesMissing
Total.Uninjured has 5912 (6.7%) missing valuesMissing
Weather.Condition has 4492 (5.1%) missing valuesMissing
Broad.phase.of.flight has 27165 (30.6%) missing valuesMissing
Report.Status has 6384 (7.2%) missing valuesMissing
Publication.Date has 13771 (15.5%) missing valuesMissing
Total.Fatal.Injuries is highly skewed (γ1 = 33.01867532)Skewed
Total.Serious.Injuries is highly skewed (γ1 = 49.40002485)Skewed
Total.Minor.Injuries is highly skewed (γ1 = 87.26947645)Skewed
Latitude is an unsupported type, check if it needs cleaning or further analysisUnsupported
Longitude is an unsupported type, check if it needs cleaning or further analysisUnsupported
Number.of.Engines has 1226 (1.4%) zerosZeros
Total.Fatal.Injuries has 59675 (67.1%) zerosZeros
Total.Serious.Injuries has 63289 (71.2%) zerosZeros
Total.Minor.Injuries has 61454 (69.1%) zerosZeros
Total.Uninjured has 29879 (33.6%) zerosZeros

Reproduction

Analysis started2024-09-19 15:30:40.719729
Analysis finished2024-09-19 15:30:48.352954
Duration7.63 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct87951
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:48.456110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1244446
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87015 ?
Unique (%)97.9%

Sample

1st row20001218X45444
2nd row20001218X45447
3rd row20061025X01555
4th row20001218X45448
5th row20041105X01764
ValueCountFrequency (%)
20001212x19172 3
 
< 0.1%
20001214x45071 3
 
< 0.1%
20001213x27982 2
 
< 0.1%
20001212x19398 2
 
< 0.1%
20001212x17181 2
 
< 0.1%
20001211x13231 2
 
< 0.1%
20041122x01848 2
 
< 0.1%
20070723x00975 2
 
< 0.1%
20001208x05272 2
 
< 0.1%
20001206x02750 2
 
< 0.1%
Other values (87941) 88867
> 99.9%
2024-09-19T10:30:48.639201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 348458
28.0%
2 229489
18.4%
1 206573
16.6%
X 85352
 
6.9%
3 73729
 
5.9%
4 66227
 
5.3%
5 50695
 
4.1%
7 47901
 
3.8%
8 46854
 
3.8%
9 44955
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1244446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 348458
28.0%
2 229489
18.4%
1 206573
16.6%
X 85352
 
6.9%
3 73729
 
5.9%
4 66227
 
5.3%
5 50695
 
4.1%
7 47901
 
3.8%
8 46854
 
3.8%
9 44955
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1244446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 348458
28.0%
2 229489
18.4%
1 206573
16.6%
X 85352
 
6.9%
3 73729
 
5.9%
4 66227
 
5.3%
5 50695
 
4.1%
7 47901
 
3.8%
8 46854
 
3.8%
9 44955
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1244446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 348458
28.0%
2 229489
18.4%
1 206573
16.6%
X 85352
 
6.9%
3 73729
 
5.9%
4 66227
 
5.3%
5 50695
 
4.1%
7 47901
 
3.8%
8 46854
 
3.8%
9 44955
 
3.6%

Investigation.Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size694.6 KiB
Accident
85015 
Incident
 
3874

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters711112
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccident
2nd rowAccident
3rd rowAccident
4th rowAccident
5th rowAccident

Common Values

ValueCountFrequency (%)
Accident 85015
95.6%
Incident 3874
 
4.4%

Length

2024-09-19T10:30:48.704492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T10:30:48.739839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
accident 85015
95.6%
incident 3874
 
4.4%

Most occurring characters

ValueCountFrequency (%)
c 173904
24.5%
n 92763
13.0%
i 88889
12.5%
d 88889
12.5%
e 88889
12.5%
t 88889
12.5%
A 85015
12.0%
I 3874
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 711112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 173904
24.5%
n 92763
13.0%
i 88889
12.5%
d 88889
12.5%
e 88889
12.5%
t 88889
12.5%
A 85015
12.0%
I 3874
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 711112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 173904
24.5%
n 92763
13.0%
i 88889
12.5%
d 88889
12.5%
e 88889
12.5%
t 88889
12.5%
A 85015
12.0%
I 3874
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 711112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 173904
24.5%
n 92763
13.0%
i 88889
12.5%
d 88889
12.5%
e 88889
12.5%
t 88889
12.5%
A 85015
12.0%
I 3874
 
0.5%
Distinct88863
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:48.876193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.020497
Min length9

Characters and Unicode

Total characters890712
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88837 ?
Unique (%)99.9%

Sample

1st rowSEA87LA080
2nd rowLAX94LA336
3rd rowNYC07LA005
4th rowLAX96LA321
5th rowCHI79FA064
ValueCountFrequency (%)
cen22la149 2
 
< 0.1%
dca23wa071 2
 
< 0.1%
wpr23la041 2
 
< 0.1%
era22la364 2
 
< 0.1%
cen23ma034 2
 
< 0.1%
dca22la135 2
 
< 0.1%
dca22wa204 2
 
< 0.1%
dca22la201 2
 
< 0.1%
gaa22wa241 2
 
< 0.1%
cen22la346 2
 
< 0.1%
Other values (88853) 88869
> 99.9%
2024-09-19T10:30:49.088158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 135499
15.2%
0 83449
 
9.4%
1 71377
 
8.0%
L 65252
 
7.3%
2 48846
 
5.5%
8 48646
 
5.5%
9 46740
 
5.2%
C 43991
 
4.9%
3 34494
 
3.9%
4 29450
 
3.3%
Other values (27) 282968
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 890712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 135499
15.2%
0 83449
 
9.4%
1 71377
 
8.0%
L 65252
 
7.3%
2 48846
 
5.5%
8 48646
 
5.5%
9 46740
 
5.2%
C 43991
 
4.9%
3 34494
 
3.9%
4 29450
 
3.3%
Other values (27) 282968
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 890712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 135499
15.2%
0 83449
 
9.4%
1 71377
 
8.0%
L 65252
 
7.3%
2 48846
 
5.5%
8 48646
 
5.5%
9 46740
 
5.2%
C 43991
 
4.9%
3 34494
 
3.9%
4 29450
 
3.3%
Other values (27) 282968
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 890712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 135499
15.2%
0 83449
 
9.4%
1 71377
 
8.0%
L 65252
 
7.3%
2 48846
 
5.5%
8 48646
 
5.5%
9 46740
 
5.2%
C 43991
 
4.9%
3 34494
 
3.9%
4 29450
 
3.3%
Other values (27) 282968
31.8%
Distinct14782
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size694.6 KiB
Minimum1948-10-24 00:00:00
Maximum2022-12-29 00:00:00
2024-09-19T10:30:49.157231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:49.208002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct27758
Distinct (%)31.2%
Missing52
Missing (%)0.1%
Memory size694.6 KiB
2024-09-19T10:30:49.333630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length63
Median length56
Mean length13.043743
Min length1

Characters and Unicode

Total characters1158767
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16801 ?
Unique (%)18.9%

Sample

1st rowMOOSE CREEK, ID
2nd rowBRIDGEPORT, CA
3rd rowSaltville, VA
4th rowEUREKA, CA
5th rowCanton, OH
ValueCountFrequency (%)
ca 8859
 
4.4%
tx 5913
 
2.9%
fl 5827
 
2.9%
ak 5672
 
2.8%
az 2834
 
1.4%
co 2730
 
1.3%
wa 2615
 
1.3%
city 2109
 
1.0%
il 2061
 
1.0%
mi 2036
 
1.0%
Other values (14112) 162442
80.0%
2024-09-19T10:30:49.532664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114532
 
9.9%
, 88855
 
7.7%
A 87911
 
7.6%
N 55643
 
4.8%
E 53820
 
4.6%
L 53670
 
4.6%
O 49966
 
4.3%
I 43133
 
3.7%
T 40180
 
3.5%
R 39988
 
3.5%
Other values (82) 531069
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1158767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
114532
 
9.9%
, 88855
 
7.7%
A 87911
 
7.6%
N 55643
 
4.8%
E 53820
 
4.6%
L 53670
 
4.6%
O 49966
 
4.3%
I 43133
 
3.7%
T 40180
 
3.5%
R 39988
 
3.5%
Other values (82) 531069
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1158767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
114532
 
9.9%
, 88855
 
7.7%
A 87911
 
7.6%
N 55643
 
4.8%
E 53820
 
4.6%
L 53670
 
4.6%
O 49966
 
4.3%
I 43133
 
3.7%
T 40180
 
3.5%
R 39988
 
3.5%
Other values (82) 531069
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1158767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
114532
 
9.9%
, 88855
 
7.7%
A 87911
 
7.6%
N 55643
 
4.8%
E 53820
 
4.6%
L 53670
 
4.6%
O 49966
 
4.3%
I 43133
 
3.7%
T 40180
 
3.5%
R 39988
 
3.5%
Other values (82) 531069
45.8%
Distinct219
Distinct (%)0.2%
Missing226
Missing (%)0.3%
Memory size694.6 KiB
2024-09-19T10:30:49.656111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length13
Mean length12.649155
Min length2

Characters and Unicode

Total characters1121512
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united 82594
47.9%
states 82251
47.7%
mexico 388
 
0.2%
brazil 374
 
0.2%
canada 359
 
0.2%
kingdom 344
 
0.2%
australia 300
 
0.2%
france 236
 
0.1%
spain 226
 
0.1%
bahamas 216
 
0.1%
Other values (241) 5033
 
2.9%
2024-09-19T10:30:49.827741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 248529
22.2%
e 167964
15.0%
a 89874
 
8.0%
i 86783
 
7.7%
n 86419
 
7.7%
d 84220
 
7.5%
s 83718
 
7.5%
83658
 
7.5%
S 82976
 
7.4%
U 82682
 
7.4%
Other values (49) 24689
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1121512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 248529
22.2%
e 167964
15.0%
a 89874
 
8.0%
i 86783
 
7.7%
n 86419
 
7.7%
d 84220
 
7.5%
s 83718
 
7.5%
83658
 
7.5%
S 82976
 
7.4%
U 82682
 
7.4%
Other values (49) 24689
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1121512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 248529
22.2%
e 167964
15.0%
a 89874
 
8.0%
i 86783
 
7.7%
n 86419
 
7.7%
d 84220
 
7.5%
s 83718
 
7.5%
83658
 
7.5%
S 82976
 
7.4%
U 82682
 
7.4%
Other values (49) 24689
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1121512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 248529
22.2%
e 167964
15.0%
a 89874
 
8.0%
i 86783
 
7.7%
n 86419
 
7.7%
d 84220
 
7.5%
s 83718
 
7.5%
83658
 
7.5%
S 82976
 
7.4%
U 82682
 
7.4%
Other values (49) 24689
 
2.2%

Latitude
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54507
Missing (%)61.3%
Memory size694.6 KiB

Longitude
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54516
Missing (%)61.3%
Memory size694.6 KiB

Airport.Code
Text

MISSING 

Distinct10374
Distinct (%)20.7%
Missing38757
Missing (%)43.6%
Memory size694.6 KiB
2024-09-19T10:30:49.996666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.1662411
Min length1

Characters and Unicode

Total characters158730
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4753 ?
Unique (%)9.5%

Sample

1st rowN58
2nd rowJAX
3rd rowT72
4th row5G6
5th rowYIP
ValueCountFrequency (%)
none 1495
 
3.0%
pvt 497
 
1.0%
apa 160
 
0.3%
ord 149
 
0.3%
mri 137
 
0.3%
den 115
 
0.2%
osh 109
 
0.2%
bjc 102
 
0.2%
vny 100
 
0.2%
ffz 98
 
0.2%
Other values (10335) 47175
94.1%
2024-09-19T10:30:50.214158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 8419
 
5.3%
A 7982
 
5.0%
S 7247
 
4.6%
O 6688
 
4.2%
L 6526
 
4.1%
C 6126
 
3.9%
E 5813
 
3.7%
M 5740
 
3.6%
T 5616
 
3.5%
K 5337
 
3.4%
Other values (63) 93236
58.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 158730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 8419
 
5.3%
A 7982
 
5.0%
S 7247
 
4.6%
O 6688
 
4.2%
L 6526
 
4.1%
C 6126
 
3.9%
E 5813
 
3.7%
M 5740
 
3.6%
T 5616
 
3.5%
K 5337
 
3.4%
Other values (63) 93236
58.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 158730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 8419
 
5.3%
A 7982
 
5.0%
S 7247
 
4.6%
O 6688
 
4.2%
L 6526
 
4.1%
C 6126
 
3.9%
E 5813
 
3.7%
M 5740
 
3.6%
T 5616
 
3.5%
K 5337
 
3.4%
Other values (63) 93236
58.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 158730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 8419
 
5.3%
A 7982
 
5.0%
S 7247
 
4.6%
O 6688
 
4.2%
L 6526
 
4.1%
C 6126
 
3.9%
E 5813
 
3.7%
M 5740
 
3.6%
T 5616
 
3.5%
K 5337
 
3.4%
Other values (63) 93236
58.7%

Airport.Name
Text

MISSING 

Distinct24870
Distinct (%)47.2%
Missing36185
Missing (%)40.7%
Memory size694.6 KiB
2024-09-19T10:30:50.378025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length21
Mean length15.687102
Min length1

Characters and Unicode

Total characters826773
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16532 ?
Unique (%)31.4%

Sample

1st rowBLACKBURN AG STRIP
2nd rowHANOVER
3rd rowJACKSONVILLE INTL
4th rowTUSKEGEE
5th rowHEARNE MUNICIPAL
ValueCountFrequency (%)
airport 8635
 
7.5%
municipal 4458
 
3.8%
county 3873
 
3.3%
field 3366
 
2.9%
muni 2322
 
2.0%
regional 1822
 
1.6%
international 1783
 
1.5%
private 1202
 
1.0%
intl 1147
 
1.0%
lake 1030
 
0.9%
Other values (10671) 86253
74.4%
2024-09-19T10:30:50.590470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63201
 
7.6%
A 53752
 
6.5%
E 39960
 
4.8%
I 39036
 
4.7%
N 39020
 
4.7%
R 37118
 
4.5%
L 35167
 
4.3%
r 31576
 
3.8%
O 31284
 
3.8%
i 29096
 
3.5%
Other values (86) 427563
51.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 826773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
63201
 
7.6%
A 53752
 
6.5%
E 39960
 
4.8%
I 39036
 
4.7%
N 39020
 
4.7%
R 37118
 
4.5%
L 35167
 
4.3%
r 31576
 
3.8%
O 31284
 
3.8%
i 29096
 
3.5%
Other values (86) 427563
51.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 826773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
63201
 
7.6%
A 53752
 
6.5%
E 39960
 
4.8%
I 39036
 
4.7%
N 39020
 
4.7%
R 37118
 
4.5%
L 35167
 
4.3%
r 31576
 
3.8%
O 31284
 
3.8%
i 29096
 
3.5%
Other values (86) 427563
51.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 826773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
63201
 
7.6%
A 53752
 
6.5%
E 39960
 
4.8%
I 39036
 
4.7%
N 39020
 
4.7%
R 37118
 
4.5%
L 35167
 
4.3%
r 31576
 
3.8%
O 31284
 
3.8%
i 29096
 
3.5%
Other values (86) 427563
51.7%

Injury.Severity
Text

MISSING 

Distinct109
Distinct (%)0.1%
Missing1000
Missing (%)1.1%
Memory size694.6 KiB
2024-09-19T10:30:50.671205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.5835201
Min length5

Characters and Unicode

Total characters754397
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)0.1%

Sample

1st rowFatal(2)
2nd rowFatal(4)
3rd rowFatal(3)
4th rowFatal(2)
5th rowFatal(1)
ValueCountFrequency (%)
non-fatal 67357
76.6%
fatal(1 6167
 
7.0%
fatal 5262
 
6.0%
fatal(2 3711
 
4.2%
incident 2219
 
2.5%
fatal(3 1147
 
1.3%
fatal(4 812
 
0.9%
fatal(5 235
 
0.3%
minor 218
 
0.2%
serious 173
 
0.2%
Other values (99) 588
 
0.7%
2024-09-19T10:30:50.807848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 170654
22.6%
t 87402
11.6%
l 85375
11.3%
F 85183
11.3%
n 72109
9.6%
o 67748
 
9.0%
N 67357
 
8.9%
- 67357
 
8.9%
( 12564
 
1.7%
) 12564
 
1.7%
Other values (23) 26084
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 754397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 170654
22.6%
t 87402
11.6%
l 85375
11.3%
F 85183
11.3%
n 72109
9.6%
o 67748
 
9.0%
N 67357
 
8.9%
- 67357
 
8.9%
( 12564
 
1.7%
) 12564
 
1.7%
Other values (23) 26084
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 754397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 170654
22.6%
t 87402
11.6%
l 85375
11.3%
F 85183
11.3%
n 72109
9.6%
o 67748
 
9.0%
N 67357
 
8.9%
- 67357
 
8.9%
( 12564
 
1.7%
) 12564
 
1.7%
Other values (23) 26084
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 754397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 170654
22.6%
t 87402
11.6%
l 85375
11.3%
F 85183
11.3%
n 72109
9.6%
o 67748
 
9.0%
N 67357
 
8.9%
- 67357
 
8.9%
( 12564
 
1.7%
) 12564
 
1.7%
Other values (23) 26084
 
3.5%

Aircraft.damage
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing3194
Missing (%)3.6%
Memory size694.6 KiB
Substantial
64148 
Destroyed
18623 
Minor
 
2805
Unknown
 
119

Length

Max length11
Median length11
Mean length10.363417
Min length5

Characters and Unicode

Total characters888093
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDestroyed
2nd rowDestroyed
3rd rowDestroyed
4th rowDestroyed
5th rowDestroyed

Common Values

ValueCountFrequency (%)
Substantial 64148
72.2%
Destroyed 18623
 
21.0%
Minor 2805
 
3.2%
Unknown 119
 
0.1%
(Missing) 3194
 
3.6%

Length

2024-09-19T10:30:50.861598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T10:30:50.900962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
substantial 64148
74.9%
destroyed 18623
 
21.7%
minor 2805
 
3.3%
unknown 119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 146919
16.5%
a 128296
14.4%
s 82771
9.3%
n 67310
7.6%
i 66953
7.5%
S 64148
7.2%
b 64148
7.2%
l 64148
7.2%
u 64148
7.2%
e 37246
 
4.2%
Other values (9) 102006
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 888093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 146919
16.5%
a 128296
14.4%
s 82771
9.3%
n 67310
7.6%
i 66953
7.5%
S 64148
7.2%
b 64148
7.2%
l 64148
7.2%
u 64148
7.2%
e 37246
 
4.2%
Other values (9) 102006
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 888093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 146919
16.5%
a 128296
14.4%
s 82771
9.3%
n 67310
7.6%
i 66953
7.5%
S 64148
7.2%
b 64148
7.2%
l 64148
7.2%
u 64148
7.2%
e 37246
 
4.2%
Other values (9) 102006
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 888093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 146919
16.5%
a 128296
14.4%
s 82771
9.3%
n 67310
7.6%
i 66953
7.5%
S 64148
7.2%
b 64148
7.2%
l 64148
7.2%
u 64148
7.2%
e 37246
 
4.2%
Other values (9) 102006
11.5%

Aircraft.Category
Categorical

IMBALANCE  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing56602
Missing (%)63.7%
Memory size694.6 KiB
Airplane
27617 
Helicopter
3440 
Glider
 
508
Balloon
 
231
Gyrocraft
 
173
Other values (10)
 
318

Length

Max length17
Median length8
Mean length8.225199
Min length3

Characters and Unicode

Total characters265567
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAirplane
2nd rowAirplane
3rd rowAirplane
4th rowAirplane
5th rowAirplane

Common Values

ValueCountFrequency (%)
Airplane 27617
31.1%
Helicopter 3440
 
3.9%
Glider 508
 
0.6%
Balloon 231
 
0.3%
Gyrocraft 173
 
0.2%
Weight-Shift 161
 
0.2%
Powered Parachute 91
 
0.1%
Ultralight 30
 
< 0.1%
Unknown 14
 
< 0.1%
WSFT 9
 
< 0.1%
Other values (5) 13
 
< 0.1%
(Missing) 56602
63.7%

Length

2024-09-19T10:30:50.948199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airplane 27617
85.3%
helicopter 3440
 
10.6%
glider 508
 
1.6%
balloon 231
 
0.7%
gyrocraft 173
 
0.5%
weight-shift 161
 
0.5%
powered 91
 
0.3%
parachute 91
 
0.3%
ultralight 30
 
0.1%
unknown 14
 
< 0.1%
Other values (6) 22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 35450
13.3%
r 32128
12.1%
l 32091
12.1%
i 31926
12.0%
p 31061
11.7%
a 28233
10.6%
n 27890
10.5%
A 27617
10.4%
o 4186
 
1.6%
t 4092
 
1.5%
Other values (25) 10893
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 265567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 35450
13.3%
r 32128
12.1%
l 32091
12.1%
i 31926
12.0%
p 31061
11.7%
a 28233
10.6%
n 27890
10.5%
A 27617
10.4%
o 4186
 
1.6%
t 4092
 
1.5%
Other values (25) 10893
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 265567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 35450
13.3%
r 32128
12.1%
l 32091
12.1%
i 31926
12.0%
p 31061
11.7%
a 28233
10.6%
n 27890
10.5%
A 27617
10.4%
o 4186
 
1.6%
t 4092
 
1.5%
Other values (25) 10893
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 265567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 35450
13.3%
r 32128
12.1%
l 32091
12.1%
i 31926
12.0%
p 31061
11.7%
a 28233
10.6%
n 27890
10.5%
A 27617
10.4%
o 4186
 
1.6%
t 4092
 
1.5%
Other values (25) 10893
 
4.1%

Registration.Number
Text

MISSING 

Distinct79104
Distinct (%)90.4%
Missing1382
Missing (%)1.6%
Memory size694.6 KiB
2024-09-19T10:30:51.092320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length5.8413156
Min length2

Characters and Unicode

Total characters511156
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72052 ?
Unique (%)82.3%

Sample

1st rowNC6404
2nd rowN5069P
3rd rowN5142R
4th rowN1168J
5th rowN15NY
ValueCountFrequency (%)
none 346
 
0.4%
unreg 131
 
0.1%
unk 14
 
< 0.1%
unknown 10
 
< 0.1%
usaf 9
 
< 0.1%
n20752 8
 
< 0.1%
n4101e 6
 
< 0.1%
n53893 6
 
< 0.1%
n8402k 6
 
< 0.1%
n121cc 6
 
< 0.1%
Other values (79101) 86987
99.4%
2024-09-19T10:30:51.310289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 86835
17.0%
1 35488
 
6.9%
2 34100
 
6.7%
3 32265
 
6.3%
5 31999
 
6.3%
4 31810
 
6.2%
6 30787
 
6.0%
7 30551
 
6.0%
9 29561
 
5.8%
8 29359
 
5.7%
Other values (53) 138401
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 511156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 86835
17.0%
1 35488
 
6.9%
2 34100
 
6.7%
3 32265
 
6.3%
5 31999
 
6.3%
4 31810
 
6.2%
6 30787
 
6.0%
7 30551
 
6.0%
9 29561
 
5.8%
8 29359
 
5.7%
Other values (53) 138401
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 511156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 86835
17.0%
1 35488
 
6.9%
2 34100
 
6.7%
3 32265
 
6.3%
5 31999
 
6.3%
4 31810
 
6.2%
6 30787
 
6.0%
7 30551
 
6.0%
9 29561
 
5.8%
8 29359
 
5.7%
Other values (53) 138401
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 511156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 86835
17.0%
1 35488
 
6.9%
2 34100
 
6.7%
3 32265
 
6.3%
5 31999
 
6.3%
4 31810
 
6.2%
6 30787
 
6.0%
7 30551
 
6.0%
9 29561
 
5.8%
8 29359
 
5.7%
Other values (53) 138401
27.1%

Make
Text

Distinct8237
Distinct (%)9.3%
Missing63
Missing (%)0.1%
Memory size694.6 KiB
2024-09-19T10:30:51.468777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length7.5983608
Min length2

Characters and Unicode

Total characters674932
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6625 ?
Unique (%)7.5%

Sample

1st rowStinson
2nd rowPiper
3rd rowCessna
4th rowRockwell
5th rowCessna
ValueCountFrequency (%)
cessna 27210
25.2%
piper 14935
 
13.8%
beech 5384
 
5.0%
boeing 2830
 
2.6%
bell 2792
 
2.6%
robinson 1688
 
1.6%
grumman 1520
 
1.4%
mooney 1385
 
1.3%
aircraft 1208
 
1.1%
american 1063
 
1.0%
Other values (6488) 47868
44.4%
2024-09-19T10:30:51.822580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 69239
 
10.3%
s 55264
 
8.2%
a 42583
 
6.3%
n 41651
 
6.2%
C 39022
 
5.8%
r 32996
 
4.9%
i 27823
 
4.1%
E 23396
 
3.5%
A 21608
 
3.2%
P 21403
 
3.2%
Other values (66) 299947
44.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 674932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 69239
 
10.3%
s 55264
 
8.2%
a 42583
 
6.3%
n 41651
 
6.2%
C 39022
 
5.8%
r 32996
 
4.9%
i 27823
 
4.1%
E 23396
 
3.5%
A 21608
 
3.2%
P 21403
 
3.2%
Other values (66) 299947
44.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 674932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 69239
 
10.3%
s 55264
 
8.2%
a 42583
 
6.3%
n 41651
 
6.2%
C 39022
 
5.8%
r 32996
 
4.9%
i 27823
 
4.1%
E 23396
 
3.5%
A 21608
 
3.2%
P 21403
 
3.2%
Other values (66) 299947
44.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 674932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 69239
 
10.3%
s 55264
 
8.2%
a 42583
 
6.3%
n 41651
 
6.2%
C 39022
 
5.8%
r 32996
 
4.9%
i 27823
 
4.1%
E 23396
 
3.5%
A 21608
 
3.2%
P 21403
 
3.2%
Other values (66) 299947
44.4%

Model
Text

Distinct12318
Distinct (%)13.9%
Missing92
Missing (%)0.1%
Memory size694.6 KiB
2024-09-19T10:30:52.001564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length5.75578
Min length1

Characters and Unicode

Total characters511096
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7615 ?
Unique (%)8.6%

Sample

1st row108-3
2nd rowPA24-180
3rd row172M
4th row112
5th row501
ValueCountFrequency (%)
152 2393
 
2.3%
172 1821
 
1.8%
172n 1166
 
1.1%
ii 1077
 
1.0%
pa-28-140 933
 
0.9%
150 865
 
0.8%
172m 798
 
0.8%
pa 696
 
0.7%
172p 692
 
0.7%
182 673
 
0.7%
Other values (9367) 91473
89.2%
2024-09-19T10:30:52.230397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 50120
 
9.8%
1 49809
 
9.7%
- 45177
 
8.8%
0 37339
 
7.3%
A 35320
 
6.9%
5 21225
 
4.2%
8 20368
 
4.0%
3 20146
 
3.9%
7 19919
 
3.9%
P 19025
 
3.7%
Other values (74) 192648
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 511096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 50120
 
9.8%
1 49809
 
9.7%
- 45177
 
8.8%
0 37339
 
7.3%
A 35320
 
6.9%
5 21225
 
4.2%
8 20368
 
4.0%
3 20146
 
3.9%
7 19919
 
3.9%
P 19025
 
3.7%
Other values (74) 192648
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 511096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 50120
 
9.8%
1 49809
 
9.7%
- 45177
 
8.8%
0 37339
 
7.3%
A 35320
 
6.9%
5 21225
 
4.2%
8 20368
 
4.0%
3 20146
 
3.9%
7 19919
 
3.9%
P 19025
 
3.7%
Other values (74) 192648
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 511096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 50120
 
9.8%
1 49809
 
9.7%
- 45177
 
8.8%
0 37339
 
7.3%
A 35320
 
6.9%
5 21225
 
4.2%
8 20368
 
4.0%
3 20146
 
3.9%
7 19919
 
3.9%
P 19025
 
3.7%
Other values (74) 192648
37.7%

Amateur.Built
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing102
Missing (%)0.1%
Memory size173.7 KiB
False
80312 
True
8475 
(Missing)
 
102
ValueCountFrequency (%)
False 80312
90.4%
True 8475
 
9.5%
(Missing) 102
 
0.1%
2024-09-19T10:30:52.291945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Number.of.Engines
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing6084
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean1.1465854
Minimum0
Maximum8
Zeros1226
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:52.325253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44651021
Coefficient of variation (CV)0.38942606
Kurtosis12.031895
Mean1.1465854
Median Absolute Deviation (MAD)0
Skewness2.5759042
Sum94943
Variance0.19937137
MonotonicityNot monotonic
2024-09-19T10:30:52.364718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 69582
78.3%
2 11079
 
12.5%
0 1226
 
1.4%
3 483
 
0.5%
4 431
 
0.5%
8 3
 
< 0.1%
6 1
 
< 0.1%
(Missing) 6084
 
6.8%
ValueCountFrequency (%)
0 1226
 
1.4%
1 69582
78.3%
2 11079
 
12.5%
3 483
 
0.5%
4 431
 
0.5%
6 1
 
< 0.1%
8 3
 
< 0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
6 1
 
< 0.1%
4 431
 
0.5%
3 483
 
0.5%
2 11079
 
12.5%
1 69582
78.3%
0 1226
 
1.4%

Engine.Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing7096
Missing (%)8.0%
Memory size694.6 KiB
Reciprocating
69530 
Turbo Shaft
 
3609
Turbo Prop
 
3391
Turbo Fan
 
2481
Unknown
 
2051
Other values (7)
 
731

Length

Max length15
Median length13
Mean length12.480286
Min length2

Characters and Unicode

Total characters1020800
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowReciprocating
2nd rowReciprocating
3rd rowReciprocating
4th rowReciprocating
5th rowTurbo Fan

Common Values

ValueCountFrequency (%)
Reciprocating 69530
78.2%
Turbo Shaft 3609
 
4.1%
Turbo Prop 3391
 
3.8%
Turbo Fan 2481
 
2.8%
Unknown 2051
 
2.3%
Turbo Jet 703
 
0.8%
Geared Turbofan 12
 
< 0.1%
Electric 10
 
< 0.1%
LR 2
 
< 0.1%
NONE 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 7096
 
8.0%

Length

2024-09-19T10:30:52.411316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reciprocating 69530
75.6%
turbo 10184
 
11.1%
shaft 3609
 
3.9%
prop 3391
 
3.7%
fan 2481
 
2.7%
unknown 2051
 
2.2%
jet 703
 
0.8%
geared 12
 
< 0.1%
turbofan 12
 
< 0.1%
electric 10
 
< 0.1%
Other values (5) 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 139081
13.6%
i 139071
13.6%
o 85169
8.3%
r 83140
8.1%
n 78176
7.7%
a 75644
7.4%
t 73853
7.2%
p 72921
7.1%
e 70268
6.9%
R 69533
6.8%
Other values (24) 133944
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1020800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 139081
13.6%
i 139071
13.6%
o 85169
8.3%
r 83140
8.1%
n 78176
7.7%
a 75644
7.4%
t 73853
7.2%
p 72921
7.1%
e 70268
6.9%
R 69533
6.8%
Other values (24) 133944
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1020800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 139081
13.6%
i 139071
13.6%
o 85169
8.3%
r 83140
8.1%
n 78176
7.7%
a 75644
7.4%
t 73853
7.2%
p 72921
7.1%
e 70268
6.9%
R 69533
6.8%
Other values (24) 133944
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1020800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 139081
13.6%
i 139071
13.6%
o 85169
8.3%
r 83140
8.1%
n 78176
7.7%
a 75644
7.4%
t 73853
7.2%
p 72921
7.1%
e 70268
6.9%
R 69533
6.8%
Other values (24) 133944
13.1%

FAR.Description
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct31
Distinct (%)0.1%
Missing56866
Missing (%)64.0%
Memory size694.6 KiB
091
18221 
Part 91: General Aviation
6486 
NUSN
 
1584
NUSC
 
1013
137
 
1010
Other values (26)
3709 

Length

Max length30
Median length3
Mean length8.3364769
Min length3

Characters and Unicode

Total characters266959
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPart 129: Foreign
2nd rowPart 91: General Aviation
3rd rowPart 91: General Aviation
4th rowPart 91: General Aviation
5th rowPart 91: General Aviation

Common Values

ValueCountFrequency (%)
091 18221
 
20.5%
Part 91: General Aviation 6486
 
7.3%
NUSN 1584
 
1.8%
NUSC 1013
 
1.1%
137 1010
 
1.1%
135 746
 
0.8%
121 679
 
0.8%
Part 137: Agricultural 437
 
0.5%
UNK 371
 
0.4%
Part 135: Air Taxi & Commuter 298
 
0.3%
Other values (21) 1178
 
1.3%
(Missing) 56866
64.0%

Length

2024-09-19T10:30:52.456704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
091 18221
33.2%
part 7525
13.7%
91 6487
 
11.8%
general 6486
 
11.8%
aviation 6486
 
11.8%
nusn 1584
 
2.9%
137 1447
 
2.6%
135 1044
 
1.9%
nusc 1013
 
1.8%
121 844
 
1.5%
Other values (39) 3771
 
6.9%

Most occurring characters

ValueCountFrequency (%)
1 29403
 
11.0%
9 25069
 
9.4%
22885
 
8.6%
a 21662
 
8.1%
0 18261
 
6.8%
r 16503
 
6.2%
t 14847
 
5.6%
i 14650
 
5.5%
e 13747
 
5.1%
n 13426
 
5.0%
Other values (43) 76506
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 266959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 29403
 
11.0%
9 25069
 
9.4%
22885
 
8.6%
a 21662
 
8.1%
0 18261
 
6.8%
r 16503
 
6.2%
t 14847
 
5.6%
i 14650
 
5.5%
e 13747
 
5.1%
n 13426
 
5.0%
Other values (43) 76506
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 266959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 29403
 
11.0%
9 25069
 
9.4%
22885
 
8.6%
a 21662
 
8.1%
0 18261
 
6.8%
r 16503
 
6.2%
t 14847
 
5.6%
i 14650
 
5.5%
e 13747
 
5.1%
n 13426
 
5.0%
Other values (43) 76506
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 266959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 29403
 
11.0%
9 25069
 
9.4%
22885
 
8.6%
a 21662
 
8.1%
0 18261
 
6.8%
r 16503
 
6.2%
t 14847
 
5.6%
i 14650
 
5.5%
e 13747
 
5.1%
n 13426
 
5.0%
Other values (43) 76506
28.7%

Schedule
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing76307
Missing (%)85.8%
Memory size694.6 KiB
NSCH
4474 
UNK
4099 
SCHD
4009 

Length

Max length4
Median length4
Mean length3.6742171
Min length3

Characters and Unicode

Total characters46229
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSCHD
2nd rowNSCH
3rd rowNSCH
4th rowSCHD
5th rowNSCH

Common Values

ValueCountFrequency (%)
NSCH 4474
 
5.0%
UNK 4099
 
4.6%
SCHD 4009
 
4.5%
(Missing) 76307
85.8%

Length

2024-09-19T10:30:52.498394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T10:30:52.533501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
nsch 4474
35.6%
unk 4099
32.6%
schd 4009
31.9%

Most occurring characters

ValueCountFrequency (%)
N 8573
18.5%
S 8483
18.3%
C 8483
18.3%
H 8483
18.3%
U 4099
8.9%
K 4099
8.9%
D 4009
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 8573
18.5%
S 8483
18.3%
C 8483
18.3%
H 8483
18.3%
U 4099
8.9%
K 4099
8.9%
D 4009
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 8573
18.5%
S 8483
18.3%
C 8483
18.3%
H 8483
18.3%
U 4099
8.9%
K 4099
8.9%
D 4009
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 8573
18.5%
S 8483
18.3%
C 8483
18.3%
H 8483
18.3%
U 4099
8.9%
K 4099
8.9%
D 4009
8.7%

Purpose.of.flight
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct26
Distinct (%)< 0.1%
Missing6192
Missing (%)7.0%
Memory size694.6 KiB
Personal
49448 
Instructional
10601 
Unknown
6802 
Aerial Application
 
4712
Business
 
4018
Other values (21)
7116 

Length

Max length25
Median length8
Mean length9.5665985
Min length4

Characters and Unicode

Total characters791129
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPersonal
2nd rowPersonal
3rd rowPersonal
4th rowPersonal
5th rowPersonal

Common Values

ValueCountFrequency (%)
Personal 49448
55.6%
Instructional 10601
 
11.9%
Unknown 6802
 
7.7%
Aerial Application 4712
 
5.3%
Business 4018
 
4.5%
Positioning 1646
 
1.9%
Other Work Use 1264
 
1.4%
Ferry 812
 
0.9%
Aerial Observation 794
 
0.9%
Public Aircraft 720
 
0.8%
Other values (16) 1880
 
2.1%
(Missing) 6192
 
7.0%

Length

2024-09-19T10:30:52.574547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
personal 49448
53.1%
instructional 10601
 
11.4%
unknown 6802
 
7.3%
aerial 5506
 
5.9%
application 4712
 
5.1%
business 4018
 
4.3%
positioning 1646
 
1.8%
other 1264
 
1.4%
work 1264
 
1.4%
use 1264
 
1.4%
Other values (26) 6605
 
7.1%

Most occurring characters

ValueCountFrequency (%)
n 104419
13.2%
o 78539
9.9%
s 76370
9.7%
r 74135
9.4%
a 73325
9.3%
l 71990
9.1%
e 65919
8.3%
P 52062
6.6%
i 38871
 
4.9%
t 32788
 
4.1%
Other values (31) 122711
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 791129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 104419
13.2%
o 78539
9.9%
s 76370
9.7%
r 74135
9.4%
a 73325
9.3%
l 71990
9.1%
e 65919
8.3%
P 52062
6.6%
i 38871
 
4.9%
t 32788
 
4.1%
Other values (31) 122711
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 791129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 104419
13.2%
o 78539
9.9%
s 76370
9.7%
r 74135
9.4%
a 73325
9.3%
l 71990
9.1%
e 65919
8.3%
P 52062
6.6%
i 38871
 
4.9%
t 32788
 
4.1%
Other values (31) 122711
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 791129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 104419
13.2%
o 78539
9.9%
s 76370
9.7%
r 74135
9.4%
a 73325
9.3%
l 71990
9.1%
e 65919
8.3%
P 52062
6.6%
i 38871
 
4.9%
t 32788
 
4.1%
Other values (31) 122711
15.5%

Air.carrier
Text

MISSING 

Distinct13590
Distinct (%)81.6%
Missing72241
Missing (%)81.3%
Memory size694.6 KiB
2024-09-19T10:30:52.680920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length90
Median length65
Mean length20.324003
Min length3

Characters and Unicode

Total characters338354
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12395 ?
Unique (%)74.5%

Sample

1st rowAir Canada
2nd rowRocky Mountain Helicopters, In
3rd rowLang Air Service
4th rowEmpire Airlines
5th rowJoel Frederick's Monarch Air
ValueCountFrequency (%)
inc 4073
 
7.6%
air 2324
 
4.3%
llc 2110
 
3.9%
aviation 1887
 
3.5%
airlines 1532
 
2.9%
dba 1178
 
2.2%
service 745
 
1.4%
flying 519
 
1.0%
helicopters 484
 
0.9%
flight 480
 
0.9%
Other values (9712) 38157
71.3%
2024-09-19T10:30:52.857685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36865
 
10.9%
i 19440
 
5.7%
e 17336
 
5.1%
A 17267
 
5.1%
r 15433
 
4.6%
n 15121
 
4.5%
a 14874
 
4.4%
I 12944
 
3.8%
t 10384
 
3.1%
L 10371
 
3.1%
Other values (85) 168319
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 338354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36865
 
10.9%
i 19440
 
5.7%
e 17336
 
5.1%
A 17267
 
5.1%
r 15433
 
4.6%
n 15121
 
4.5%
a 14874
 
4.4%
I 12944
 
3.8%
t 10384
 
3.1%
L 10371
 
3.1%
Other values (85) 168319
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 338354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36865
 
10.9%
i 19440
 
5.7%
e 17336
 
5.1%
A 17267
 
5.1%
r 15433
 
4.6%
n 15121
 
4.5%
a 14874
 
4.4%
I 12944
 
3.8%
t 10384
 
3.1%
L 10371
 
3.1%
Other values (85) 168319
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 338354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36865
 
10.9%
i 19440
 
5.7%
e 17336
 
5.1%
A 17267
 
5.1%
r 15433
 
4.6%
n 15121
 
4.5%
a 14874
 
4.4%
I 12944
 
3.8%
t 10384
 
3.1%
L 10371
 
3.1%
Other values (85) 168319
49.7%

Total.Fatal.Injuries
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct125
Distinct (%)0.2%
Missing11401
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean0.64785515
Minimum0
Maximum349
Zeros59675
Zeros (%)67.1%
Negative0
Negative (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:52.922258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum349
Range349
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.4859601
Coefficient of variation (CV)8.4678807
Kurtosis1355.6006
Mean0.64785515
Median Absolute Deviation (MAD)0
Skewness33.018675
Sum50201
Variance30.095758
MonotonicityNot monotonic
2024-09-19T10:30:52.970342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59675
67.1%
1 8883
 
10.0%
2 5173
 
5.8%
3 1589
 
1.8%
4 1103
 
1.2%
5 346
 
0.4%
6 216
 
0.2%
7 101
 
0.1%
8 70
 
0.1%
10 45
 
0.1%
Other values (115) 287
 
0.3%
(Missing) 11401
 
12.8%
ValueCountFrequency (%)
0 59675
67.1%
1 8883
 
10.0%
2 5173
 
5.8%
3 1589
 
1.8%
4 1103
 
1.2%
5 346
 
0.4%
6 216
 
0.2%
7 101
 
0.1%
8 70
 
0.1%
9 42
 
< 0.1%
ValueCountFrequency (%)
349 2
< 0.1%
295 1
< 0.1%
270 1
< 0.1%
265 1
< 0.1%
256 1
< 0.1%
239 1
< 0.1%
230 1
< 0.1%
229 1
< 0.1%
228 2
< 0.1%
224 1
< 0.1%

Total.Serious.Injuries
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct50
Distinct (%)0.1%
Missing12510
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean0.2798806
Minimum0
Maximum161
Zeros63289
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:53.017500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum161
Range161
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5440836
Coefficient of variation (CV)5.5169371
Kurtosis3737.3087
Mean0.2798806
Median Absolute Deviation (MAD)0
Skewness49.400025
Sum21377
Variance2.3841943
MonotonicityNot monotonic
2024-09-19T10:30:53.068316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63289
71.2%
1 9125
 
10.3%
2 2815
 
3.2%
3 629
 
0.7%
4 258
 
0.3%
5 78
 
0.1%
6 41
 
< 0.1%
7 27
 
< 0.1%
9 16
 
< 0.1%
8 13
 
< 0.1%
Other values (40) 88
 
0.1%
(Missing) 12510
 
14.1%
ValueCountFrequency (%)
0 63289
71.2%
1 9125
 
10.3%
2 2815
 
3.2%
3 629
 
0.7%
4 258
 
0.3%
5 78
 
0.1%
6 41
 
< 0.1%
7 27
 
< 0.1%
8 13
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
161 1
< 0.1%
137 1
< 0.1%
125 1
< 0.1%
106 1
< 0.1%
88 1
< 0.1%
81 1
< 0.1%
67 1
< 0.1%
63 1
< 0.1%
60 1
< 0.1%
59 2
< 0.1%

Total.Minor.Injuries
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct57
Distinct (%)0.1%
Missing11933
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean0.35706118
Minimum0
Maximum380
Zeros61454
Zeros (%)69.1%
Negative0
Negative (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:53.118439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum380
Range380
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2356253
Coefficient of variation (CV)6.2611828
Kurtosis12365.048
Mean0.35706118
Median Absolute Deviation (MAD)0
Skewness87.269476
Sum27478
Variance4.9980206
MonotonicityNot monotonic
2024-09-19T10:30:53.166360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61454
69.1%
1 10320
 
11.6%
2 3576
 
4.0%
3 784
 
0.9%
4 372
 
0.4%
5 129
 
0.1%
6 67
 
0.1%
7 59
 
0.1%
9 22
 
< 0.1%
8 20
 
< 0.1%
Other values (47) 153
 
0.2%
(Missing) 11933
 
13.4%
ValueCountFrequency (%)
0 61454
69.1%
1 10320
 
11.6%
2 3576
 
4.0%
3 784
 
0.9%
4 372
 
0.4%
5 129
 
0.1%
6 67
 
0.1%
7 59
 
0.1%
8 20
 
< 0.1%
9 22
 
< 0.1%
ValueCountFrequency (%)
380 1
< 0.1%
200 1
< 0.1%
171 1
< 0.1%
125 1
< 0.1%
96 1
< 0.1%
84 1
< 0.1%
71 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
65 1
< 0.1%

Total.Uninjured
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct379
Distinct (%)0.5%
Missing5912
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean5.3254396
Minimum0
Maximum699
Zeros29879
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size694.6 KiB
2024-09-19T10:30:53.213511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum699
Range699
Interquartile range (IQR)2

Descriptive statistics

Standard deviation27.913634
Coefficient of variation (CV)5.2415644
Kurtosis104.61249
Mean5.3254396
Median Absolute Deviation (MAD)1
Skewness9.0861041
Sum441889
Variance779.17099
MonotonicityNot monotonic
2024-09-19T10:30:53.261781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29879
33.6%
1 25101
28.2%
2 15988
18.0%
3 4313
 
4.9%
4 2662
 
3.0%
5 887
 
1.0%
6 500
 
0.6%
7 281
 
0.3%
8 163
 
0.2%
9 128
 
0.1%
Other values (369) 3075
 
3.5%
(Missing) 5912
 
6.7%
ValueCountFrequency (%)
0 29879
33.6%
1 25101
28.2%
2 15988
18.0%
3 4313
 
4.9%
4 2662
 
3.0%
5 887
 
1.0%
6 500
 
0.6%
7 281
 
0.3%
8 163
 
0.2%
9 128
 
0.1%
ValueCountFrequency (%)
699 2
< 0.1%
588 2
< 0.1%
576 1
< 0.1%
573 2
< 0.1%
558 1
< 0.1%
528 2
< 0.1%
521 1
< 0.1%
507 1
< 0.1%
501 2
< 0.1%
495 2
< 0.1%

Weather.Condition
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing4492
Missing (%)5.1%
Memory size694.6 KiB
VMC
77303 
IMC
 
5976
UNK
 
856
Unk
 
262

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters253191
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNK
2nd rowUNK
3rd rowIMC
4th rowIMC
5th rowVMC

Common Values

ValueCountFrequency (%)
VMC 77303
87.0%
IMC 5976
 
6.7%
UNK 856
 
1.0%
Unk 262
 
0.3%
(Missing) 4492
 
5.1%

Length

2024-09-19T10:30:53.306147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T10:30:53.342113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
vmc 77303
91.6%
imc 5976
 
7.1%
unk 1118
 
1.3%

Most occurring characters

ValueCountFrequency (%)
M 83279
32.9%
C 83279
32.9%
V 77303
30.5%
I 5976
 
2.4%
U 1118
 
0.4%
N 856
 
0.3%
K 856
 
0.3%
n 262
 
0.1%
k 262
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 83279
32.9%
C 83279
32.9%
V 77303
30.5%
I 5976
 
2.4%
U 1118
 
0.4%
N 856
 
0.3%
K 856
 
0.3%
n 262
 
0.1%
k 262
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 83279
32.9%
C 83279
32.9%
V 77303
30.5%
I 5976
 
2.4%
U 1118
 
0.4%
N 856
 
0.3%
K 856
 
0.3%
n 262
 
0.1%
k 262
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 83279
32.9%
C 83279
32.9%
V 77303
30.5%
I 5976
 
2.4%
U 1118
 
0.4%
N 856
 
0.3%
K 856
 
0.3%
n 262
 
0.1%
k 262
 
0.1%

Broad.phase.of.flight
Categorical

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing27165
Missing (%)30.6%
Memory size694.6 KiB
Landing
15428 
Takeoff
12493 
Cruise
10269 
Maneuvering
8144 
Approach
6546 
Other values (7)
8844 

Length

Max length11
Median length9
Mean length7.3616746
Min length4

Characters and Unicode

Total characters454392
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCruise
2nd rowUnknown
3rd rowCruise
4th rowCruise
5th rowApproach

Common Values

ValueCountFrequency (%)
Landing 15428
17.4%
Takeoff 12493
14.1%
Cruise 10269
 
11.6%
Maneuvering 8144
 
9.2%
Approach 6546
 
7.4%
Climb 2034
 
2.3%
Taxi 1958
 
2.2%
Descent 1887
 
2.1%
Go-around 1353
 
1.5%
Standing 945
 
1.1%
Other values (2) 667
 
0.8%
(Missing) 27165
30.6%

Length

2024-09-19T10:30:53.384867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
landing 15428
25.0%
takeoff 12493
20.2%
cruise 10269
16.6%
maneuvering 8144
13.2%
approach 6546
10.6%
climb 2034
 
3.3%
taxi 1958
 
3.2%
descent 1887
 
3.1%
go-around 1353
 
2.2%
standing 945
 
1.5%
Other values (2) 667
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 53918
 
11.9%
a 46867
 
10.3%
e 42943
 
9.5%
i 38778
 
8.5%
r 26431
 
5.8%
f 24986
 
5.5%
g 24517
 
5.4%
o 22293
 
4.9%
u 19766
 
4.3%
d 17726
 
3.9%
Other values (23) 136167
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 454392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 53918
 
11.9%
a 46867
 
10.3%
e 42943
 
9.5%
i 38778
 
8.5%
r 26431
 
5.8%
f 24986
 
5.5%
g 24517
 
5.4%
o 22293
 
4.9%
u 19766
 
4.3%
d 17726
 
3.9%
Other values (23) 136167
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 454392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 53918
 
11.9%
a 46867
 
10.3%
e 42943
 
9.5%
i 38778
 
8.5%
r 26431
 
5.8%
f 24986
 
5.5%
g 24517
 
5.4%
o 22293
 
4.9%
u 19766
 
4.3%
d 17726
 
3.9%
Other values (23) 136167
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 454392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 53918
 
11.9%
a 46867
 
10.3%
e 42943
 
9.5%
i 38778
 
8.5%
r 26431
 
5.8%
f 24986
 
5.5%
g 24517
 
5.4%
o 22293
 
4.9%
u 19766
 
4.3%
d 17726
 
3.9%
Other values (23) 136167
30.0%

Report.Status
Text

MISSING 

Distinct17074
Distinct (%)20.7%
Missing6384
Missing (%)7.2%
Memory size694.6 KiB
2024-09-19T10:30:53.515263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1271
Median length14
Mean length46.162839
Min length1

Characters and Unicode

Total characters3808665
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16566 ?
Unique (%)20.1%

Sample

1st rowProbable Cause
2nd rowProbable Cause
3rd rowProbable Cause
4th rowProbable Cause
5th rowProbable Cause
ValueCountFrequency (%)
cause 61819
 
10.7%
probable 61780
 
10.7%
the 46805
 
8.1%
to 21533
 
3.7%
of 17093
 
3.0%
a 16001
 
2.8%
in 12516
 
2.2%
and 11138
 
1.9%
failure 10012
 
1.7%
pilot's 9427
 
1.6%
Other values (7515) 307858
53.4%
2024-09-19T10:30:53.722217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
496638
13.0%
e 387731
 
10.2%
a 317126
 
8.3%
o 246796
 
6.5%
i 226677
 
6.0%
t 225545
 
5.9%
r 218937
 
5.7%
n 214769
 
5.6%
l 197086
 
5.2%
s 173379
 
4.6%
Other values (87) 1103981
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3808665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
496638
13.0%
e 387731
 
10.2%
a 317126
 
8.3%
o 246796
 
6.5%
i 226677
 
6.0%
t 225545
 
5.9%
r 218937
 
5.7%
n 214769
 
5.6%
l 197086
 
5.2%
s 173379
 
4.6%
Other values (87) 1103981
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3808665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
496638
13.0%
e 387731
 
10.2%
a 317126
 
8.3%
o 246796
 
6.5%
i 226677
 
6.0%
t 225545
 
5.9%
r 218937
 
5.7%
n 214769
 
5.6%
l 197086
 
5.2%
s 173379
 
4.6%
Other values (87) 1103981
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3808665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
496638
13.0%
e 387731
 
10.2%
a 317126
 
8.3%
o 246796
 
6.5%
i 226677
 
6.0%
t 225545
 
5.9%
r 218937
 
5.7%
n 214769
 
5.6%
l 197086
 
5.2%
s 173379
 
4.6%
Other values (87) 1103981
29.0%

Publication.Date
Date

MISSING 

Distinct2924
Distinct (%)3.9%
Missing13771
Missing (%)15.5%
Memory size694.6 KiB
Minimum1980-04-16 00:00:00
Maximum2022-12-30 00:00:00
2024-09-19T10:30:53.789416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:53.841510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-09-19T10:30:46.959750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:45.961444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.296733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.516369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.741145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:47.005192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.008944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.341328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.562852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.786881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:47.047962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.052818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.385357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.605908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.829646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:47.095775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.096131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.429538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.651753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.874595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:47.142257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.252403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.473075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.697107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T10:30:46.918426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-19T10:30:53.881286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Aircraft.CategoryAircraft.damageAmateur.BuiltBroad.phase.of.flightEngine.TypeFAR.DescriptionInvestigation.TypeNumber.of.EnginesPurpose.of.flightScheduleTotal.Fatal.InjuriesTotal.Minor.InjuriesTotal.Serious.InjuriesTotal.UninjuredWeather.Condition
Aircraft.Category1.0000.1020.1860.0950.3970.2810.0640.3710.1290.2380.0000.0000.0000.0080.023
Aircraft.damage0.1021.0000.0540.2690.2250.2920.8410.2060.1580.3610.0390.0230.0120.1990.158
Amateur.Built0.1860.0541.0000.0950.1120.1810.0620.1290.2190.1220.0050.0080.0000.0440.073
Broad.phase.of.flight0.0950.2690.0951.0000.1080.1330.1200.0960.1290.4600.0040.0000.0000.0540.184
Engine.Type0.3970.2250.1120.1081.0000.4570.4660.6340.2120.5330.0430.0310.0300.2170.129
FAR.Description0.2810.2920.1810.1330.4571.0000.5560.2760.3360.8390.1060.2110.0670.2360.363
Investigation.Type0.0640.8410.0620.1200.4660.5561.0000.4040.3210.5240.0000.0150.0060.3510.075
Number.of.Engines0.3710.2060.1290.0960.6340.2760.4041.0000.2030.5180.062-0.024-0.0240.1920.107
Purpose.of.flight0.1290.1580.2190.1290.2120.3360.3210.2031.0000.6270.0240.0050.0040.1120.126
Schedule0.2380.3610.1220.4600.5330.8390.5240.5180.6271.0000.0900.0280.0580.4030.183
Total.Fatal.Injuries0.0000.0390.0050.0040.0430.1060.0000.0620.0240.0901.000-0.119-0.041-0.5020.042
Total.Minor.Injuries0.0000.0230.0080.0000.0310.2110.015-0.0240.0050.028-0.1191.0000.099-0.2630.023
Total.Serious.Injuries0.0000.0120.0000.0000.0300.0670.006-0.0240.0040.058-0.0410.0991.000-0.2800.024
Total.Uninjured0.0080.1990.0440.0540.2170.2360.3510.1920.1120.403-0.502-0.263-0.2801.0000.052
Weather.Condition0.0230.1580.0730.1840.1290.3630.0750.1070.1260.1830.0420.0230.0240.0521.000

Missing values

2024-09-19T10:30:47.259638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-19T10:30:47.528726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-19T10:30:48.123870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Event.IdInvestigation.TypeAccident.NumberEvent.DateLocationCountryLatitudeLongitudeAirport.CodeAirport.NameInjury.SeverityAircraft.damageAircraft.CategoryRegistration.NumberMakeModelAmateur.BuiltNumber.of.EnginesEngine.TypeFAR.DescriptionSchedulePurpose.of.flightAir.carrierTotal.Fatal.InjuriesTotal.Serious.InjuriesTotal.Minor.InjuriesTotal.UninjuredWeather.ConditionBroad.phase.of.flightReport.StatusPublication.Date
020001218X45444AccidentSEA87LA0801948-10-24MOOSE CREEK, IDUnited StatesNaNNaNNaNNaNFatal(2)DestroyedNaNNC6404Stinson108-3No1.0ReciprocatingNaNNaNPersonalNaN2.00.00.00.0UNKCruiseProbable CauseNaN
120001218X45447AccidentLAX94LA3361962-07-19BRIDGEPORT, CAUnited StatesNaNNaNNaNNaNFatal(4)DestroyedNaNN5069PPiperPA24-180No1.0ReciprocatingNaNNaNPersonalNaN4.00.00.00.0UNKUnknownProbable Cause19-09-1996
220061025X01555AccidentNYC07LA0051974-08-30Saltville, VAUnited States36.922223-81.878056NaNNaNFatal(3)DestroyedNaNN5142RCessna172MNo1.0ReciprocatingNaNNaNPersonalNaN3.0NaNNaNNaNIMCCruiseProbable Cause26-02-2007
320001218X45448AccidentLAX96LA3211977-06-19EUREKA, CAUnited StatesNaNNaNNaNNaNFatal(2)DestroyedNaNN1168JRockwell112No1.0ReciprocatingNaNNaNPersonalNaN2.00.00.00.0IMCCruiseProbable Cause12-09-2000
420041105X01764AccidentCHI79FA0641979-08-02Canton, OHUnited StatesNaNNaNNaNNaNFatal(1)DestroyedNaNN15NYCessna501NoNaNNaNNaNNaNPersonalNaN1.02.0NaN0.0VMCApproachProbable Cause16-04-1980
520170710X52551AccidentNYC79AA1061979-09-17BOSTON, MAUnited States42.445277-70.758333NaNNaNNon-FatalSubstantialAirplaneCF-TLUMcdonnell DouglasDC9No2.0Turbo FanPart 129: ForeignSCHDNaNAir CanadaNaNNaN1.044.0VMCClimbProbable Cause19-09-2017
620001218X45446AccidentCHI81LA1061981-08-01COTTON, MNUnited StatesNaNNaNNaNNaNFatal(4)DestroyedNaNN4988ECessna180No1.0ReciprocatingNaNNaNPersonalNaN4.00.00.00.0IMCUnknownProbable Cause06-11-2001
720020909X01562AccidentSEA82DA0221982-01-01PULLMAN, WAUnited StatesNaNNaNNaNBLACKBURN AG STRIPNon-FatalSubstantialAirplaneN2482NCessna140No1.0ReciprocatingPart 91: General AviationNaNPersonalNaN0.00.00.02.0VMCTakeoffProbable Cause01-01-1982
820020909X01561AccidentNYC82DA0151982-01-01EAST HANOVER, NJUnited StatesNaNNaNN58HANOVERNon-FatalSubstantialAirplaneN7967QCessna401BNo2.0ReciprocatingPart 91: General AviationNaNBusinessNaN0.00.00.02.0IMCLandingProbable Cause01-01-1982
920020909X01560AccidentMIA82DA0291982-01-01JACKSONVILLE, FLUnited StatesNaNNaNJAXJACKSONVILLE INTLNon-FatalSubstantialNaNN3906KNorth AmericanNAVION L-17BNo1.0ReciprocatingNaNNaNPersonalNaN0.00.03.00.0IMCCruiseProbable Cause01-01-1982
Event.IdInvestigation.TypeAccident.NumberEvent.DateLocationCountryLatitudeLongitudeAirport.CodeAirport.NameInjury.SeverityAircraft.damageAircraft.CategoryRegistration.NumberMakeModelAmateur.BuiltNumber.of.EnginesEngine.TypeFAR.DescriptionSchedulePurpose.of.flightAir.carrierTotal.Fatal.InjuriesTotal.Serious.InjuriesTotal.Minor.InjuriesTotal.UninjuredWeather.ConditionBroad.phase.of.flightReport.StatusPublication.Date
8887920221219106472AccidentDCA23LA0962022-12-18Kahului, HIUnited StatesNaNNaNNaNNaNNaNNaNNaNN393HAAIRBUSA330-243NoNaNNaN121SCHDNaNHAWAIIAN AIRLINES INC0.00.00.00.0NaNNaNNaNNaN
8888020221219106477AccidentWPR23LA0712022-12-18San Manual, AZUnited StatesNaNNaNNaNNaNNon-FatalNaNNaNN4144PPIPERPA28NoNaNNaN091NaNPersonalChandler Air Service0.00.00.03.0NaNNaNNaN20-12-2022
8888120221221106483AccidentCEN23LA0672022-12-21Auburn Hills, MIUnited StatesNaNNaNNaNNaNMinorNaNNaNN8786UCESSNA172FNoNaNNaN091NSCHPersonalPilot0.01.00.00.0NaNNaNNaN22-12-2022
8888220221222106486AccidentCEN23LA0682022-12-21Reserve, LAUnited StatesNaNNaNNaNNaNMinorNaNNaNN321GDGRUMMAN AMERICAN AVN. CORP.AA-5BNoNaNNaN091NaNInstructionalNaN0.01.00.01.0NaNNaNNaN27-12-2022
8888320221228106502AccidentGAA23WA0462022-12-22Brasnorte,BrazilNaNNaNNaNNaNFatalNaNNaNPP-IRCAIR TRACTORAT502NoNaNNaNNaNNaNNaNNaN1.00.00.00.0NaNNaNNaN28-12-2022
8888420221227106491AccidentERA23LA0932022-12-26Annapolis, MDUnited StatesNaNNaNNaNNaNMinorNaNNaNN1867HPIPERPA-28-151NoNaNNaN091NaNPersonalNaN0.01.00.00.0NaNNaNNaN29-12-2022
8888520221227106494AccidentERA23LA0952022-12-26Hampton, NHUnited StatesNaNNaNNaNNaNNaNNaNNaNN2895ZBELLANCA7ECANoNaNNaNNaNNaNNaNNaN0.00.00.00.0NaNNaNNaNNaN
8888620221227106497AccidentWPR23LA0752022-12-26Payson, AZUnited States341525N1112021WPANPAYSONNon-FatalSubstantialAirplaneN749PJAMERICAN CHAMPION AIRCRAFT8GCBCNo1.0NaN091NaNPersonalNaN0.00.00.01.0VMCNaNNaN27-12-2022
8888720221227106498AccidentWPR23LA0762022-12-26Morgan, UTUnited StatesNaNNaNNaNNaNNaNNaNNaNN210CUCESSNA210NNoNaNNaN091NaNPersonalMC CESSNA 210N LLC0.00.00.00.0NaNNaNNaNNaN
8888820221230106513AccidentERA23LA0972022-12-29Athens, GAUnited StatesNaNNaNNaNNaNMinorNaNNaNN9026PPIPERPA-24-260NoNaNNaN091NaNPersonalNaN0.01.00.01.0NaNNaNNaN30-12-2022